How AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
The tiny device uses a tightly focused beam of light to capture and manipulate cells.
Modeling relationships to solve complex problems efficiently
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
MIT named No. 2 university by U.S. News for 2024-25
Undergraduate engineering is No. 1; undergraduate business and computer science programs are No. 2.
Microelectronics projects awarded CHIPS and Science Act funding
MIT and Lincoln Laboratory are among awardees of $38 million in project awards to the Northeast Microelectronics Coalition to boost U.S. chip technology innovation.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
AI pareidolia: Can machines spot faces in inanimate objects?
New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.
Enhancing LLM collaboration for smarter, more efficient solutions
“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.